Policy learning can be used to extract individualized treatment regimes from observational data in healthcare, civics, e-commerce, and beyond. One big hurdle to policy learning is a commonplace lack of overlap in the data for different actions, which can lead to unwieldy policy evaluation and poorly performing learned policies. We study a solution to this problem based on retargeting, that is, changing the population on which policies are optimized. We first argue that at the population level, retargeting may induce little to no bias. We then characterize the optimal reference policy centering and retargeting weights in both binary-action and multi-action settings. We do this in terms of the asymptotic efficient estimation variance of the new learning objective. We further consider bias regularization. Extensive empirical results in a simulation study and a case study of targeted job counseling demonstrate that retargeting is a fairly easy way to significantly improve any policy learning procedure.

中文翻译：

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通过最佳重定向实现更有效的策略学习

政策学习可用于从医疗保健、公民、电子商务等领域的观察数据中提取个性化治疗方案。政策学习的一大障碍是不同行动的数据普遍缺乏重叠，这可能导致政策评估笨拙和学习政策表现不佳。我们研究了基于重新定位的这个问题的解决方案，即改变策略优化的人群。我们首先认为，在人口水平上，重新定位可能几乎不会引起偏见。然后，我们在二元动作和多动作设置中表征最佳参考策略中心和重定向权重。我们根据新学习目标的渐近有效估计方差来做到这一点。我们进一步考虑偏差正则化。